Robust decision-making (RDM) is an iterative decision analytics framework that aims to help identify potential robust strategies, characterize the vulnerabilities of such strategies, and evaluate the tradeoffs among them.[1][2][3] RDM focuses on informing decisions under conditions of what is called "deep uncertainty", that is, conditions where the parties to a decision do not know or do not agree on the system models relating actions to consequences or the prior probability distributions for the key input parameters to those models.[2]: 1011
Robust decision making describes a variety of approaches that differ from traditional optimum expected utility analysis in that they characterize uncertainty with multiple representations of the future rather than a single set of probability distributions and use robustness, rather than optimality, as a decision criterion. (1011-1012)
Robust decision making is more analytical than intuitive. It adopts a systematic approach to remove uncertainty within the resources available to make safe and effective decisions. (1023)